Abstract Modern analytical methods allow simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. Analysis of metabolomics data is a multi-step process that requires variety of bioinformatics and statistical tools. One of the biggest challenges in metabolomics is how alterations in metabolite levels can be linked to specific biological processes that are disrupted contributing to the development of disease, or reflecting the disease state. To address this challenge, we propose to develop methods and build computational tools to help researchers interrogate their metabolomics data and integrate them with other molecular phenotypes to build testable hypotheses and derive biological knowledge that could help addressing this challenge. Our team has extensive collaborative experience working together in the Phase I Common Fund-supported Regional Comprehensive Metabolomics Resource Core (MRC2) and in building computational methods and tools for the analysis of multi-dimensional `omics data. We propose to build on our past efforts to develop a novel functional enrichment testing (FET) approach that will not be limited to compounds found in canonical metabolic pathways and will include both known and unknown metabolites in the analysis. We will leverage our previously developed methodology for building partial correlation networks that allows identifying commonalities and differences in network structures derived from different experimental conditions. Exploring relationships between key metabolic changes and alterations in transcript, proteins and other molecular components can provide additional levels of information and help build biological insights from experimental data. The overarching goal of this proposal is to develop FET methods that would enable analysis of multi-condition, multi-layer `omics data sets. To that end, we propose a network-based data integration strategy that will help uncover relationships both within and between different molecular layers, identify subnetworks, containing metabolites, transcripts etc., and test their significance. We anticipate that the application of our methods will lead to better insights into molecular networks affected by many complex diseases. !